library(survival)
library(FRESA.CAD)
## Loading required package: Rcpp
## Loading required package: stringr
## Loading required package: miscTools
## Loading required package: Hmisc
##
## Attaching package: 'Hmisc'
## The following objects are masked from 'package:base':
##
## format.pval, units
## Loading required package: pROC
## Type 'citation("pROC")' for a citation.
##
## Attaching package: 'pROC'
## The following objects are masked from 'package:stats':
##
## cov, smooth, var
op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('keep.trailing.zeros',TRUE)
data(lung)
## Warning in data(lung): data set 'lung' not found
lung$inst <- NULL
lung$status <- lung$status - 1
lung <- lung[complete.cases(lung),]
pander::pander(table(lung$status))
| 0 | 1 |
|---|---|
| 47 | 121 |
pander::pander(summary(lung$time))
| Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. |
|---|---|---|---|---|---|
| 5 | 175 | 268 | 310 | 416 | 1022 |
convar <- colnames(lung)[lapply(apply(lung,2,unique),length) > 10]
convar <- convar[convar != "time"]
topvar <- univariate_BinEnsemble(lung[,c("status",convar)],"status")
pander::pander(topvar)
| age | wt.loss |
|---|---|
| 0.106 | 0.106 |
topv <- min(5,length(topvar))
topFive <- names(topvar)[1:topv]
RRanalysis <- list();
idx <- 1
for (topf in topFive)
{
RRanalysis[[idx]] <- RRPlot(cbind(lung$status,lung[,topf]),
atProb=c(0.90),
timetoEvent=lung$time,
title=topf,
# plotRR=FALSE
)
idx <- idx + 1
}
names(RRanalysis) <- topFive
ROCAUC <- NULL
CstatCI <- NULL
RRatios <- NULL
LogRangp <- NULL
Sensitivity <- NULL
Specificity <- NULL
for (topf in topFive)
{
CstatCI <- rbind(CstatCI,RRanalysis[[topf]]$c.index$cstatCI)
RRatios <- rbind(RRatios,RRanalysis[[topf]]$RR_atP)
LogRangp <- rbind(LogRangp,RRanalysis[[topf]]$surdif$pvalue)
Sensitivity <- rbind(Sensitivity,RRanalysis[[topf]]$ROCAnalysis$sensitivity)
Specificity <- rbind(Specificity,RRanalysis[[topf]]$ROCAnalysis$specificity)
ROCAUC <- rbind(ROCAUC,RRanalysis[[topf]]$ROCAnalysis$aucs)
}
rownames(CstatCI) <- topFive
rownames(RRatios) <- topFive
rownames(LogRangp) <- topFive
rownames(Sensitivity) <- topFive
rownames(Specificity) <- topFive
rownames(ROCAUC) <- topFive
pander::pander(ROCAUC)
| est | lower | upper | |
|---|---|---|---|
| age | 0.588 | 0.491 | 0.686 |
| wt.loss | 0.552 | 0.453 | 0.650 |
pander::pander(CstatCI)
| mean.C Index | median | lower | upper | |
|---|---|---|---|---|
| age | 0.558 | 0.558 | 0.499 | 0.62 |
| wt.loss | 0.508 | 0.508 | 0.447 | 0.57 |
pander::pander(RRatios)
| est | lower | upper | |
|---|---|---|---|
| age | 1.016 | 0.748 | 1.38 |
| wt.loss | 0.785 | 0.462 | 1.33 |
pander::pander(LogRangp)
| age | 0.513 |
| wt.loss | 0.358 |
pander::pander(Sensitivity)
| est | lower | upper | |
|---|---|---|---|
| age | 0.0992 | 0.0523 | 0.167 |
| wt.loss | 0.0496 | 0.0184 | 0.105 |
pander::pander(Specificity)
| est | lower | upper | |
|---|---|---|---|
| age | 0.894 | 0.769 | 0.965 |
| wt.loss | 0.894 | 0.769 | 0.965 |
meanMatrix <- cbind(ROCAUC[,1],CstatCI[,1],Sensitivity[,1],Specificity[,1],RRatios[,1])
colnames(meanMatrix) <- c("ROCAUC","C-Stat","Sen","Spe","RR")
pander::pander(meanMatrix)
| ROCAUC | C-Stat | Sen | Spe | RR | |
|---|---|---|---|---|---|
| age | 0.588 | 0.558 | 0.0992 | 0.894 | 1.016 |
| wt.loss | 0.552 | 0.508 | 0.0496 | 0.894 | 0.785 |
ml <- BSWiMS.model(Surv(time,status)~1,data=lung,NumberofRepeats = 10)
[++++++++++++++++++++++++++++++]…
sm <- summary(ml)
pander::pander(sm$coefficients)
| Estimate | lower | HR | upper | u.Accuracy | r.Accuracy | |
|---|---|---|---|---|---|---|
| ph.ecog | 4.32e-01 | 1.194 | 1.541 | 1.988 | 0.679 | 0.649 |
| sex | -4.59e-01 | 0.456 | 0.632 | 0.876 | 0.649 | 0.679 |
| pat.karno | -1.77e-03 | 0.997 | 0.998 | 1.000 | 0.506 | 0.720 |
| ph.karno | -5.80e-07 | 1.000 | 1.000 | 1.000 | 0.577 | 0.720 |
| full.Accuracy | u.AUC | r.AUC | full.AUC | IDI | NRI | |
|---|---|---|---|---|---|---|
| ph.ecog | 0.601 | 0.601 | 0.620 | 0.600 | 0.0449 | 0.405 |
| sex | 0.601 | 0.620 | 0.601 | 0.600 | 0.0285 | 0.478 |
| pat.karno | 0.506 | 0.585 | 0.500 | 0.585 | 0.0292 | 0.342 |
| ph.karno | 0.577 | 0.570 | 0.500 | 0.570 | 0.0143 | 0.280 |
| z.IDI | z.NRI | Delta.AUC | Frequency | |
|---|---|---|---|---|
| ph.ecog | 3.33 | 2.48 | -0.02005 | 1 |
| sex | 2.76 | 2.85 | -0.00167 | 1 |
| pat.karno | 2.44 | 2.24 | 0.08546 | 1 |
| ph.karno | 2.22 | 1.64 | 0.06998 | 1 |
Here we evaluate the model using the RRPlot() function.
Here we will use the predicted event probability assuming a baseline hazard for events withing 5 years
timeinterval <- 2*mean(subset(lung,status==1)$time)
h0 <- sum(lung$status & lung$time <= timeinterval)
h0 <- h0/sum((lung$time > timeinterval) | (lung$status==1))
pander::pander(t(c(h0=h0,timeinterval=timeinterval)),caption="Initial Parameters")
| h0 | timeinterval |
|---|---|
| 0.85 | 578 |
index <- predict(ml,lung)
rdata <- cbind(lung$status,ppoisGzero(index,h0))
rrAnalysisTrain <- RRPlot(rdata,atProb=c(0.90),
timetoEvent=lung$time,
title="Raw Train: Lung Cancer",
ysurvlim=c(0.00,1.0),
riskTimeInterval=timeinterval)
As we can see the Observed probability as well as the Time vs. Events are not calibrated.
pander::pander(t(rrAnalysisTrain$keyPoints),caption="Threshold values")
| @:0.9 | @MAX_BACC | @MAX_RR | @SPE100 | p(0.5) | |
|---|---|---|---|---|---|
| Thr | 0.649 | 0.475 | 0.339 | 0.339 | 0.493 |
| RR | 1.280 | 1.789 | 60.500 | 60.500 | 1.312 |
| SEN | 0.306 | 0.843 | 1.000 | 1.000 | 0.636 |
| SPE | 0.872 | 0.489 | 0.170 | 0.170 | 0.596 |
| BACC | 0.589 | 0.666 | 0.585 | 0.585 | 0.616 |
pander::pander(t(rrAnalysisTrain$OERatio$estimate),caption="O/E Ratio")
| O/E | Low | Upper | p.value |
|---|---|---|---|
| 1.65 | 1.37 | 1.97 | 3.16e-07 |
pander::pander(t(rrAnalysisTrain$OE95ci),caption="O/E Mean")
| mean | 50% | 2.5% | 97.5% |
|---|---|---|---|
| 1.23 | 1.23 | 1.19 | 1.27 |
pander::pander(t(rrAnalysisTrain$OAcum95ci),caption="O/Acum Mean")
| mean | 50% | 2.5% | 97.5% |
|---|---|---|---|
| 1.21 | 1.21 | 1.2 | 1.22 |
pander::pander(rrAnalysisTrain$c.index$cstatCI,caption="C. Index")
| mean.C Index | median | lower | upper |
|---|---|---|---|
| 0.652 | 0.653 | 0.59 | 0.71 |
pander::pander(t(rrAnalysisTrain$ROCAnalysis$aucs),caption="ROC AUC")
| est | lower | upper |
|---|---|---|
| 0.691 | 0.598 | 0.784 |
pander::pander((rrAnalysisTrain$ROCAnalysis$sensitivity),caption="Sensitivity")
| est | lower | upper |
|---|---|---|
| 0.306 | 0.225 | 0.396 |
pander::pander((rrAnalysisTrain$ROCAnalysis$specificity),caption="Specificity")
| est | lower | upper |
|---|---|---|
| 0.872 | 0.743 | 0.952 |
pander::pander(t(rrAnalysisTrain$thr_atP),caption="Probability Thresholds")
| 90% |
|---|
| 0.649 |
pander::pander(t(rrAnalysisTrain$RR_atP),caption="Risk Ratio")
| est | lower | upper |
|---|---|---|
| 1.28 | 1.08 | 1.52 |
pander::pander(rrAnalysisTrain$surdif,caption="Logrank test")
| N | Observed | Expected | (O-E)^2/E | (O-E)^2/V | |
|---|---|---|---|---|---|
| class=0 | 125 | 84 | 96.5 | 1.61 | 8.01 |
| class=1 | 43 | 37 | 24.5 | 6.34 | 8.01 |
op <- par(no.readonly = TRUE)
calprob <- CoxRiskCalibration(ml,lung,"status","time")
pander::pander(c(h0=calprob$h0,
Gain=calprob$hazardGain,
DeltaTime=calprob$timeInterval),
caption="Cox Calibration Parameters")
| h0 | Gain | DeltaTime |
|---|---|---|
| 1.29 | 1.52 | 749 |
h0 <- calprob$h0
timeinterval <- calprob$timeInterval;
rdata <- cbind(lung$status,calprob$prob)
rrAnalysisTrain <- RRPlot(rdata,atProb=c(0.90),
timetoEvent=lung$time,
title="Train: Lung",
ysurvlim=c(0.00,1.0),
riskTimeInterval=timeinterval)
pander::pander(t(rrAnalysisTrain$keyPoints),caption="Threshold values")
| @:0.9 | @MAX_BACC | @MAX_RR | @SPE100 | p(0.5) | |
|---|---|---|---|---|---|
| Thr | 0.796 | 0.624 | 0.467 | 0.467 | 0.479 |
| RR | 1.280 | 1.789 | 60.500 | 60.500 | 2.784 |
| SEN | 0.306 | 0.843 | 1.000 | 1.000 | 0.959 |
| SPE | 0.872 | 0.489 | 0.170 | 0.170 | 0.277 |
| BACC | 0.589 | 0.666 | 0.585 | 0.585 | 0.618 |
pander::pander(t(rrAnalysisTrain$OERatio$estimate),caption="O/E Ratio")
| O/E | Low | Upper | p.value |
|---|---|---|---|
| 1.45 | 1.2 | 1.73 | 0.000124 |
pander::pander(t(rrAnalysisTrain$OE95ci),caption="O/E Mean")
| mean | 50% | 2.5% | 97.5% |
|---|---|---|---|
| 1.06 | 1.06 | 1.02 | 1.1 |
pander::pander(t(rrAnalysisTrain$OAcum95ci),caption="O/Acum Mean")
| mean | 50% | 2.5% | 97.5% |
|---|---|---|---|
| 1.01 | 1.01 | 1 | 1.01 |
pander::pander(rrAnalysisTrain$c.index$cstatCI,caption="C. Index")
| mean.C Index | median | lower | upper |
|---|---|---|---|
| 0.652 | 0.652 | 0.592 | 0.712 |
pander::pander(t(rrAnalysisTrain$ROCAnalysis$aucs),caption="ROC AUC")
| est | lower | upper |
|---|---|---|
| 0.691 | 0.598 | 0.784 |
pander::pander((rrAnalysisTrain$ROCAnalysis$sensitivity),caption="Sensitivity")
| est | lower | upper |
|---|---|---|
| 0.306 | 0.225 | 0.396 |
pander::pander((rrAnalysisTrain$ROCAnalysis$specificity),caption="Specificity")
| est | lower | upper |
|---|---|---|
| 0.872 | 0.743 | 0.952 |
pander::pander(t(rrAnalysisTrain$thr_atP),caption="Probability Thresholds")
| 90% |
|---|
| 0.796 |
pander::pander(t(rrAnalysisTrain$RR_atP),caption="Risk Ratio")
| est | lower | upper |
|---|---|---|
| 1.28 | 1.08 | 1.52 |
pander::pander(rrAnalysisTrain$surdif,caption="Logrank test")
| N | Observed | Expected | (O-E)^2/E | (O-E)^2/V | |
|---|---|---|---|---|---|
| class=0 | 125 | 84 | 96.5 | 1.61 | 8.01 |
| class=1 | 43 | 37 | 24.5 | 6.34 | 8.01 |
rcv <- randomCV(theData=lung,
theOutcome = Surv(time,status)~1,
fittingFunction=BSWiMS.model,
trainFraction = 0.95,
repetitions=200,
classSamplingType = "Pro"
)
.[++].[++].[+++].[++].[+++].[+++].[+++].[+++].[++].[+++]10 Tested: 69 Avg. Selected: 3.6 Min Tests: 1 Max Tests: 5 Mean Tests: 1.449275 . MAD: 0.4785205
.[+++].[+++].[+++].[+++].[+++].[+++].[+++].[+++].[+++].[++]20 Tested: 110 Avg. Selected: 3.75 Min Tests: 1 Max Tests: 7 Mean Tests: 1.818182 . MAD: 0.4732616
.[++].[+++].[+++].[++++].[+++].[+].[++].[+++].[+++].[++++]30 Tested: 141 Avg. Selected: 3.766667 Min Tests: 1 Max Tests: 8 Mean Tests: 2.12766 . MAD: 0.4730391
.[++].[+++].[++++].[++-].[++++].[+++].[+++].[+++].[+++].[+++]40 Tested: 151 Avg. Selected: 3.825 Min Tests: 1 Max Tests: 10 Mean Tests: 2.649007 . MAD: 0.4718192
.[+++].[++].[+++].[+++].[+++].[+++].[+++].[+++].[+++].[++]50 Tested: 159 Avg. Selected: 3.82 Min Tests: 1 Max Tests: 10 Mean Tests: 3.144654 . MAD: 0.4751851
.[++-].[+++].[++-].[++].[+++].[+++].[+++].[+++].[++].[+++]60 Tested: 160 Avg. Selected: 3.783333 Min Tests: 1 Max Tests: 10 Mean Tests: 3.75 . MAD: 0.4739128
.[+++].[+++].[+++].[+].[+].[+++].[++].[+++].[+++].[+++]70 Tested: 164 Avg. Selected: 3.742857 Min Tests: 1 Max Tests: 10 Mean Tests: 4.268293 . MAD: 0.4742704
.[++].[+++].[+++].[+++].[+++].[++].[++].[++].[++].[+++]80 Tested: 168 Avg. Selected: 3.7125 Min Tests: 1 Max Tests: 10 Mean Tests: 4.761905 . MAD: 0.4752243
.[+++].[+++].[+++].[+++].[++].[+++].[++].[++].[+++].[++++]90 Tested: 168 Avg. Selected: 3.722222 Min Tests: 1 Max Tests: 12 Mean Tests: 5.357143 . MAD: 0.4750161
.[+++].[+].[+++].[++++].[+++].[++-].[+++].[+++].[+++].[+++]100 Tested: 168 Avg. Selected: 3.73 Min Tests: 1 Max Tests: 13 Mean Tests: 5.952381 . MAD: 0.4746718
.[+-].[+].[+++].[+++].[++].[++].[+++].[++-].[+++].[++]110 Tested: 168 Avg. Selected: 3.681818 Min Tests: 1 Max Tests: 14 Mean Tests: 6.547619 . MAD: 0.4751729
.[++].[++++].[++].[+++].[+++].[++-].[++++].[++].[++-].[+++]120 Tested: 168 Avg. Selected: 3.675 Min Tests: 1 Max Tests: 15 Mean Tests: 7.142857 . MAD: 0.4750718
.[+++].[++++].[+++].[+++].[+++].[++].[++].[+++].[++].[+++]130 Tested: 168 Avg. Selected: 3.684615 Min Tests: 2 Max Tests: 15 Mean Tests: 7.738095 . MAD: 0.4748304
.[++].[+++].[+++].[+++].[+++].[++].[++].[++-].[+++].[+++]140 Tested: 168 Avg. Selected: 3.678571 Min Tests: 2 Max Tests: 17 Mean Tests: 8.333333 . MAD: 0.4746694
.[+++].[++-].[+++].[+++].[+++].[++].[+++].[++].[+++].[+++]150 Tested: 168 Avg. Selected: 3.68 Min Tests: 3 Max Tests: 18 Mean Tests: 8.928571 . MAD: 0.4751227
.[++].[++].[+++].[+++].[+++].[++].[+++].[+++].[+++].[++]160 Tested: 168 Avg. Selected: 3.675 Min Tests: 3 Max Tests: 19 Mean Tests: 9.52381 . MAD: 0.4750523
.[+++-].[+++-].[+++].[++++].[+++].[+++].[++++].[+++].[++-].[++-]170 Tested: 168 Avg. Selected: 3.694118 Min Tests: 4 Max Tests: 20 Mean Tests: 10.11905 . MAD: 0.4749435
.[++].[+++].[++].[++].[++].[++].[++].[+++].[++].[+++]180 Tested: 168 Avg. Selected: 3.672222 Min Tests: 4 Max Tests: 22 Mean Tests: 10.71429 . MAD: 0.474824
.[+++].[+++].[+++].[+++].[+++].[+++].[++].[+++].[+++].[++]190 Tested: 168 Avg. Selected: 3.678947 Min Tests: 4 Max Tests: 23 Mean Tests: 11.30952 . MAD: 0.4750101
.[+++].[+++].[++].[+++].[+++].[+++].[+++].[++].[+++].[+++]200 Tested: 168 Avg. Selected: 3.685 Min Tests: 4 Max Tests: 27 Mean Tests: 11.90476 . MAD: 0.4751702
stp <- rcv$survTestPredictions
stp <- stp[!is.na(stp[,4]),]
bbx <- boxplot(unlist(stp[,1])~rownames(stp),plot=FALSE)
times <- bbx$stats[3,]
status <- boxplot(unlist(stp[,2])~rownames(stp),plot=FALSE)$stats[3,]
prob <- ppoisGzero(boxplot(unlist(stp[,4])~rownames(stp),plot=FALSE)$stats[3,],h0)
rdatacv <- cbind(status,prob)
rownames(rdatacv) <- bbx$names
names(times) <- bbx$names
rrAnalysisTest <- RRPlot(rdatacv,atProb=c(0.90),
timetoEvent=times,
title="Test: Lung Cancer",
ysurvlim=c(0.00,1.0),
riskTimeInterval=timeinterval)
pander::pander(t(rrAnalysisTest$keyPoints),caption="Threshold values")
| @:0.9 | @MAX_BACC | @MAX_RR | @SPE100 | p(0.5) | |
|---|---|---|---|---|---|
| Thr | 0.808 | 0.607 | 0.472 | 0.449 | 0.507 |
| RR | 1.186 | 2.958 | 3.299 | 1.000 | 2.784 |
| SEN | 0.198 | 0.959 | 0.975 | 1.000 | 0.959 |
| SPE | 0.894 | 0.298 | 0.213 | 0.000 | 0.277 |
| BACC | 0.546 | 0.628 | 0.594 | 0.500 | 0.618 |
pander::pander(t(rrAnalysisTest$OERatio$estimate),caption="O/E Ratio")
| O/E | Low | Upper | p.value |
|---|---|---|---|
| 1.44 | 1.2 | 1.72 | 0.000126 |
pander::pander(t(rrAnalysisTest$OE95ci),caption="O/E Mean")
| mean | 50% | 2.5% | 97.5% |
|---|---|---|---|
| 1.05 | 1.05 | 1.01 | 1.09 |
pander::pander(t(rrAnalysisTest$OAcum95ci),caption="O/Acum Mean")
| mean | 50% | 2.5% | 97.5% |
|---|---|---|---|
| 0.961 | 0.961 | 0.95 | 0.971 |
pander::pander(rrAnalysisTest$c.index$cstatCI,caption="C. Index")
| mean.C Index | median | lower | upper |
|---|---|---|---|
| 0.609 | 0.61 | 0.546 | 0.668 |
pander::pander(t(rrAnalysisTest$ROCAnalysis$aucs),caption="ROC AUC")
| est | lower | upper |
|---|---|---|
| 0.62 | 0.522 | 0.718 |
pander::pander((rrAnalysisTest$ROCAnalysis$sensitivity),caption="Sensitivity")
| est | lower | upper |
|---|---|---|
| 0.198 | 0.131 | 0.281 |
pander::pander((rrAnalysisTest$ROCAnalysis$specificity),caption="Specificity")
| est | lower | upper |
|---|---|---|
| 0.894 | 0.769 | 0.965 |
pander::pander(t(rrAnalysisTest$thr_atP),caption="Probability Thresholds")
| 90% |
|---|
| 0.807 |
pander::pander(t(rrAnalysisTest$RR_atP),caption="Risk Ratio")
| est | lower | upper |
|---|---|---|
| 1.19 | 0.972 | 1.45 |
pander::pander(rrAnalysisTest$surdif,caption="Logrank test")
| N | Observed | Expected | (O-E)^2/E | (O-E)^2/V | |
|---|---|---|---|---|---|
| class=0 | 139 | 97 | 103.4 | 0.397 | 2.77 |
| class=1 | 29 | 24 | 17.6 | 2.334 | 2.77 |
rdatacv <- cbind(status,prob,times)
calprob <- CalibrationProbPoissonRisk(rdatacv)
pander::pander(c(h0=calprob$h0,
Gain=calprob$hazardGain,
DeltaTime=calprob$timeInterval),
caption="Cox Calibration Parameters")
| h0 | Gain | DeltaTime |
|---|---|---|
| 0.85 | 1 | 755 |
timeinterval <- calprob$timeInterval;
rdata <- cbind(status,calprob$prob)
rrAnalysisTest <- RRPlot(rdata,atProb=c(0.90),
timetoEvent=times,
title="Calibrated Test: Lung",
ysurvlim=c(0.00,1.0),
riskTimeInterval=timeinterval)
pander::pander(t(rrAnalysisTest$keyPoints),caption="Threshold values")
| @:0.9 | @MAX_BACC | @MAX_RR | @SPE100 | p(0.5) | |
|---|---|---|---|---|---|
| Thr | 0.808 | 0.607 | 0.472 | 0.449 | 0.507 |
| RR | 1.186 | 2.958 | 3.299 | 1.000 | 2.784 |
| SEN | 0.198 | 0.959 | 0.975 | 1.000 | 0.959 |
| SPE | 0.894 | 0.298 | 0.213 | 0.000 | 0.277 |
| BACC | 0.546 | 0.628 | 0.594 | 0.500 | 0.618 |
pander::pander(t(rrAnalysisTest$OERatio$estimate),caption="O/E Ratio")
| O/E | Low | Upper | p.value |
|---|---|---|---|
| 1.45 | 1.2 | 1.73 | 9.67e-05 |
pander::pander(t(rrAnalysisTest$OE95ci),caption="O/E Mean")
| mean | 50% | 2.5% | 97.5% |
|---|---|---|---|
| 1.06 | 1.06 | 1.02 | 1.09 |
pander::pander(t(rrAnalysisTest$OAcum95ci),caption="O/Acum Mean")
| mean | 50% | 2.5% | 97.5% |
|---|---|---|---|
| 0.961 | 0.96 | 0.952 | 0.969 |
pander::pander(rrAnalysisTest$c.index$cstatCI,caption="C. Index")
| mean.C Index | median | lower | upper |
|---|---|---|---|
| 0.609 | 0.609 | 0.551 | 0.671 |
pander::pander(t(rrAnalysisTest$ROCAnalysis$aucs),caption="ROC AUC")
| est | lower | upper |
|---|---|---|
| 0.62 | 0.522 | 0.718 |
pander::pander((rrAnalysisTest$ROCAnalysis$sensitivity),caption="Sensitivity")
| est | lower | upper |
|---|---|---|
| 0.198 | 0.131 | 0.281 |
pander::pander((rrAnalysisTest$ROCAnalysis$specificity),caption="Specificity")
| est | lower | upper |
|---|---|---|
| 0.894 | 0.769 | 0.965 |
pander::pander(t(rrAnalysisTest$thr_atP),caption="Probability Thresholds")
| 90% |
|---|
| 0.807 |
pander::pander(t(rrAnalysisTest$RR_atP),caption="Risk Ratio")
| est | lower | upper |
|---|---|---|
| 1.19 | 0.972 | 1.45 |
pander::pander(rrAnalysisTest$surdif,caption="Logrank test")
| N | Observed | Expected | (O-E)^2/E | (O-E)^2/V | |
|---|---|---|---|---|---|
| class=0 | 139 | 97 | 103.4 | 0.397 | 2.77 |
| class=1 | 29 | 24 | 17.6 | 2.334 | 2.77 |